Application of microscale wind and detailed wind power plant data in large-scale wind generation simulations

Matti Juhani Koivisto*, Konstantinos Plakas, Ernesto Rodrigo Hurtado Ellmann, Neil Davis, Poul Sørensen

*Corresponding author for this work

    Research output: Contribution to journalJournal articleResearchpeer-review

    99 Downloads (Pure)

    Abstract

    With increasing wind installations, there is a need to analyse wind generation variability in detail. This paper applies the reanalysis approach for modelling the variability; however, with two important additions. Firstly, high-resolution microscale data is combined with mesoscale reanalysis time series to model local variability in wind. Secondly, as there are often missing technical parameters in large-scale wind power plant datasets, machine learning is used to estimate the missing values. It is shown that such detailed modelling leads to more accurate simulations than a baseline model when compared to historical data from multiple European countries. In addition, applicability of the methodology for analysing future scenarios with changing wind installations is demonstrated.
    Original languageEnglish
    Article number106638
    JournalElectric Power Systems Research
    Volume190
    Issue numberS1
    Number of pages7
    ISSN0378-7796
    DOIs
    Publication statusPublished - 2021
    EventXXI Power Systems Computation Conference - ONLINE EVENT, Porto, Portugal
    Duration: 29 Jun 20203 Jul 2020
    Conference number: 21
    https://pscc2020.pt/

    Conference

    ConferenceXXI Power Systems Computation Conference
    Number21
    LocationONLINE EVENT
    Country/TerritoryPortugal
    CityPorto
    Period29/06/202003/07/2020
    Internet address

    Keywords

    • Microscale
    • Random forest
    • Reanalysis
    • Variability
    • Wind

    Fingerprint

    Dive into the research topics of 'Application of microscale wind and detailed wind power plant data in large-scale wind generation simulations'. Together they form a unique fingerprint.

    Cite this